The present embodiments relate to remote maintenance of rolling stocks. Information from rolling stock, such as trains or trucks, is monitored to identify problems or incidents that may need maintenance.
In a typical condition-based maintenance setting, monitored units are equipped with a series of sensors and message-emitting devices. In rolling stocks, a control system located on the train or trucks is constantly monitoring raw sensor readings and creating messages (e.g., alerts) in case sensor readings are below or above predefined thresholds. Each generated messages has a code which defines severity and the unit subsystem to which the message is applicable. Additionally, each message has a timestamp corresponding to the time when the problem or warning occurred. The messages and sensor readings are transferred from the train to a monitoring station on a regular basis.
Fleet dispatchers use the messages and sensor readings as an overview of how the fleet performs and to take actions (e.g. issue maintenance or repair orders). The data is collected and constantly analyzed. Diagnosis messages are flowing to the monitoring station at a high rate and large volume across a fleet's units. Dispatchers are confronted with the cumbersome task of manually filtering out and grouping together various messages and sensor data in order to identify actual events or events requiring further action. With large fleets, the identification of events is even more burdensome. Some of the messages or sensor readings may be mere anomalies, associated with expected occurrences or otherwise not of interest.
To assist analysis, a set of business rules or statistical models which have been learned utilizing available historical data are applied to the data. However, these rules or models may not accurately group the information, resulting in extra work for dispatchers.